Gene regulatory network analysis for triple-negative breast neoplasms by using gene expression data

Hee Chan Jung, Sung Hwan Kim, Jeong Hoon Lee, Ju Han Kim, Sung Won Han

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Purpose: To better identify the physiology of triple-negative breast neoplasm (TNBN), we analyzed the TNBN gene regulatory network using gene expression data. Methods: We collected TNBN gene expression data from The Cancer Genome Atlas to construct a TNBN gene regulatory network using least absolute shrinkage and selection operator regression. In addition, we constructed a triple-positive breast neoplasm (TPBN) network for comparison. Furthermore, survival analysis based on gene expression levels and differentially expressed gene (DEG) analysis were carried out to support and compare the network analysis results, respectively. Results: The TNBN gene regulatory network, which followed a power-law distribution, had 10,237 vertices and 17,773 edges, with an average vertex-to-vertex distance of 8.6. The genes ZDHHC20 and RAPGEF6 were identified by centrality analysis to be important vertices. However, in the DEG analysis, we could not find meaningful fold changes in ZDHHC20 and RAPGEF6 between the TPBN and TNBN gene expression data. In the multivariate survival analysis, the hazard ratio for ZDHHC20 and RAPGEF6 was 1.677 (1.192–2.357) and 1.676 (1.222–2.299), respectively. Conclusion: Our TNBN gene regulatory network was a scale-free one, which means that the network would be easily destroyed if the hub vertices were attacked. Thus, it is important to identify the hub vertices in the network analysis. In the TNBN gene regulatory network, ZDHHC20 and RAPGEF6 were found to be oncogenes. Further study of these genes could help to reveal a novel method for treating TNBN in the future.

Original languageEnglish
Pages (from-to)240-245
Number of pages6
JournalJournal of Breast Cancer
Volume20
Issue number3
DOIs
Publication statusPublished - 2017 Sep 1

Fingerprint

Triple Negative Breast Neoplasms
Gene Regulatory Networks
Neoplasm Genes
Gene Expression
Survival Analysis
Genes
Breast Neoplasms
Atlases
Oncogenes
Multivariate Analysis
Genome

Keywords

  • Genes
  • Oncogenes
  • Triple negative breast neoplasms

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Gene regulatory network analysis for triple-negative breast neoplasms by using gene expression data. / Jung, Hee Chan; Kim, Sung Hwan; Lee, Jeong Hoon; Kim, Ju Han; Han, Sung Won.

In: Journal of Breast Cancer, Vol. 20, No. 3, 01.09.2017, p. 240-245.

Research output: Contribution to journalArticle

Jung, Hee Chan ; Kim, Sung Hwan ; Lee, Jeong Hoon ; Kim, Ju Han ; Han, Sung Won. / Gene regulatory network analysis for triple-negative breast neoplasms by using gene expression data. In: Journal of Breast Cancer. 2017 ; Vol. 20, No. 3. pp. 240-245.
@article{94fcff07462e4a718afebbd2ec393c56,
title = "Gene regulatory network analysis for triple-negative breast neoplasms by using gene expression data",
abstract = "Purpose: To better identify the physiology of triple-negative breast neoplasm (TNBN), we analyzed the TNBN gene regulatory network using gene expression data. Methods: We collected TNBN gene expression data from The Cancer Genome Atlas to construct a TNBN gene regulatory network using least absolute shrinkage and selection operator regression. In addition, we constructed a triple-positive breast neoplasm (TPBN) network for comparison. Furthermore, survival analysis based on gene expression levels and differentially expressed gene (DEG) analysis were carried out to support and compare the network analysis results, respectively. Results: The TNBN gene regulatory network, which followed a power-law distribution, had 10,237 vertices and 17,773 edges, with an average vertex-to-vertex distance of 8.6. The genes ZDHHC20 and RAPGEF6 were identified by centrality analysis to be important vertices. However, in the DEG analysis, we could not find meaningful fold changes in ZDHHC20 and RAPGEF6 between the TPBN and TNBN gene expression data. In the multivariate survival analysis, the hazard ratio for ZDHHC20 and RAPGEF6 was 1.677 (1.192–2.357) and 1.676 (1.222–2.299), respectively. Conclusion: Our TNBN gene regulatory network was a scale-free one, which means that the network would be easily destroyed if the hub vertices were attacked. Thus, it is important to identify the hub vertices in the network analysis. In the TNBN gene regulatory network, ZDHHC20 and RAPGEF6 were found to be oncogenes. Further study of these genes could help to reveal a novel method for treating TNBN in the future.",
keywords = "Genes, Oncogenes, Triple negative breast neoplasms",
author = "Jung, {Hee Chan} and Kim, {Sung Hwan} and Lee, {Jeong Hoon} and Kim, {Ju Han} and Han, {Sung Won}",
year = "2017",
month = "9",
day = "1",
doi = "10.4048/jbc.2017.20.3.240",
language = "English",
volume = "20",
pages = "240--245",
journal = "Journal of Breast Cancer",
issn = "1738-6756",
publisher = "Korean Breast Cancer Society",
number = "3",

}

TY - JOUR

T1 - Gene regulatory network analysis for triple-negative breast neoplasms by using gene expression data

AU - Jung, Hee Chan

AU - Kim, Sung Hwan

AU - Lee, Jeong Hoon

AU - Kim, Ju Han

AU - Han, Sung Won

PY - 2017/9/1

Y1 - 2017/9/1

N2 - Purpose: To better identify the physiology of triple-negative breast neoplasm (TNBN), we analyzed the TNBN gene regulatory network using gene expression data. Methods: We collected TNBN gene expression data from The Cancer Genome Atlas to construct a TNBN gene regulatory network using least absolute shrinkage and selection operator regression. In addition, we constructed a triple-positive breast neoplasm (TPBN) network for comparison. Furthermore, survival analysis based on gene expression levels and differentially expressed gene (DEG) analysis were carried out to support and compare the network analysis results, respectively. Results: The TNBN gene regulatory network, which followed a power-law distribution, had 10,237 vertices and 17,773 edges, with an average vertex-to-vertex distance of 8.6. The genes ZDHHC20 and RAPGEF6 were identified by centrality analysis to be important vertices. However, in the DEG analysis, we could not find meaningful fold changes in ZDHHC20 and RAPGEF6 between the TPBN and TNBN gene expression data. In the multivariate survival analysis, the hazard ratio for ZDHHC20 and RAPGEF6 was 1.677 (1.192–2.357) and 1.676 (1.222–2.299), respectively. Conclusion: Our TNBN gene regulatory network was a scale-free one, which means that the network would be easily destroyed if the hub vertices were attacked. Thus, it is important to identify the hub vertices in the network analysis. In the TNBN gene regulatory network, ZDHHC20 and RAPGEF6 were found to be oncogenes. Further study of these genes could help to reveal a novel method for treating TNBN in the future.

AB - Purpose: To better identify the physiology of triple-negative breast neoplasm (TNBN), we analyzed the TNBN gene regulatory network using gene expression data. Methods: We collected TNBN gene expression data from The Cancer Genome Atlas to construct a TNBN gene regulatory network using least absolute shrinkage and selection operator regression. In addition, we constructed a triple-positive breast neoplasm (TPBN) network for comparison. Furthermore, survival analysis based on gene expression levels and differentially expressed gene (DEG) analysis were carried out to support and compare the network analysis results, respectively. Results: The TNBN gene regulatory network, which followed a power-law distribution, had 10,237 vertices and 17,773 edges, with an average vertex-to-vertex distance of 8.6. The genes ZDHHC20 and RAPGEF6 were identified by centrality analysis to be important vertices. However, in the DEG analysis, we could not find meaningful fold changes in ZDHHC20 and RAPGEF6 between the TPBN and TNBN gene expression data. In the multivariate survival analysis, the hazard ratio for ZDHHC20 and RAPGEF6 was 1.677 (1.192–2.357) and 1.676 (1.222–2.299), respectively. Conclusion: Our TNBN gene regulatory network was a scale-free one, which means that the network would be easily destroyed if the hub vertices were attacked. Thus, it is important to identify the hub vertices in the network analysis. In the TNBN gene regulatory network, ZDHHC20 and RAPGEF6 were found to be oncogenes. Further study of these genes could help to reveal a novel method for treating TNBN in the future.

KW - Genes

KW - Oncogenes

KW - Triple negative breast neoplasms

UR - http://www.scopus.com/inward/record.url?scp=85030638005&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85030638005&partnerID=8YFLogxK

U2 - 10.4048/jbc.2017.20.3.240

DO - 10.4048/jbc.2017.20.3.240

M3 - Article

AN - SCOPUS:85030638005

VL - 20

SP - 240

EP - 245

JO - Journal of Breast Cancer

JF - Journal of Breast Cancer

SN - 1738-6756

IS - 3

ER -